1. Technical Field
The present disclosure relates to segmentation and, more specifically, to structure segmentation via MAR.
2. Discussion of Related Art
Segmentation involves the processing of digital image data to identify two or more distinct objects within the image. In the field of medical imaging, segmentation may be used to differentiate between different anatomical structures such as organs. Segmentation may also be applied to other computer vision problems such as identification of vehicles or people from still or video images.
In the field of medical imaging, segmentation may be particularly useful in identifying suspicious lesions that may be potentially malignant by distinguishing between multiple organs within the medical image so that organ-specific search strategies may be used for identifying suspicious lesions.
Segmentation may be performed on either two-dimensional images such as x-rays or three-dimensional images such as MRIs and CT scans. Prior to performing segmentation, the image data may be in digital form and this can be accomplished by either acquiring the medical image directly into digital form or by digitizing a conventional image. In either case, the resulting digital image data is expressed as a set of pixels (in the case of two-dimensional image data) or voxels (in the case of three-dimensional image data) with varying intensities.
Conventionally, segmentation is performed with extensive manual input, for example, a user such as a radiologist or other medical practitioner, may be prompted to highlight an approximate boundary for a given anatomical structure in one or more two-dimensional image slices and one or more algorithms may be used to ascertain the bounds of the entire anatomical structure. Such segmentation techniques are known to be computationally intensive, and thus may require extensive processing capabilities, long processing times and/or may be prone to errors.
One such technique for performing segmentation involves a growing algorithm where a user first selects a seed point within the structure to be segmented. The selected seed point is understood to be part of the structure and each voxel neighboring the seed point is analyzed for potential inclusion into the structure. If a neighboring voxel satisfies certain predetermined conditions such as having an intensity value close to the one or more voxels already included in the structure or are found not to have characteristics of a boundary, then that neighboring voxel may be added to the structure, and the voxels neighboring that voxel may be analyzed for potential inclusion as well. In this way, the process may be performed recursively until the entire structure is segmented.
Segmentation using growing algorithms, however, may be prone to an error known as “leakage” where voxels that are not truly part of the structure get misidentified as being part of the structure or voxels that are part of the structure get misidentified as being not part of the structure. This sort of error is known as leakage because it can result from a single voxel outside of the structure being misidentified as part of the structure, and then more erroneously characterized voxels may grow from this single voxel. Once the segmentation growth has breached the true boundary between structures, there may be little to stop the growing segmentation from fully encompassing proximate structures.
The extent of leakage may be minimized by taking into consideration prior knowledge such as the approximate size of the structure being segmented so that growth does not occur beyond a predetermined perimeter. However, after leakage has occurred, the user may have to manually adjust the segmentation to ensure that no voxels have been mischaracterized.